""" Model creation / weight loading / state_dict helpers Hacked together by / Copyright 2020 Ross Wightman """ import logging import os import math from collections import OrderedDict from copy import deepcopy from typing import Any, Callable, Optional, Tuple import torch import torch.nn as nn from torch.hub import load_state_dict_from_url from .features import FeatureListNet, FeatureDictNet, FeatureHookNet from .fx_features import FeatureGraphNet from .hub import has_hf_hub, download_cached_file, load_state_dict_from_hf from .layers import Conv2dSame, Linear _logger = logging.getLogger(__name__) def load_state_dict(checkpoint_path, use_ema=False): if checkpoint_path and os.path.isfile(checkpoint_path): checkpoint = torch.load(checkpoint_path, map_location='cpu') state_dict_key = '' if isinstance(checkpoint, dict): if use_ema and checkpoint.get('state_dict_ema', None) is not None: state_dict_key = 'state_dict_ema' elif use_ema and checkpoint.get('model_ema', None) is not None: state_dict_key = 'model_ema' elif 'state_dict' in checkpoint: state_dict_key = 'state_dict' elif 'model' in checkpoint: state_dict_key = 'model' if state_dict_key: state_dict = checkpoint[state_dict_key] new_state_dict = OrderedDict() for k, v in state_dict.items(): # strip `module.` prefix name = k[7:] if k.startswith('module') else k new_state_dict[name] = v state_dict = new_state_dict else: state_dict = checkpoint _logger.info("Loaded {} from checkpoint '{}'".format(state_dict_key, checkpoint_path)) return state_dict else: _logger.error("No checkpoint found at '{}'".format(checkpoint_path)) raise FileNotFoundError() def load_checkpoint(model, checkpoint_path, use_ema=False, strict=True): if os.path.splitext(checkpoint_path)[-1].lower() in ('.npz', '.npy'): # numpy checkpoint, try to load via model specific load_pretrained fn if hasattr(model, 'load_pretrained'): model.load_pretrained(checkpoint_path) else: raise NotImplementedError('Model cannot load numpy checkpoint') return state_dict = load_state_dict(checkpoint_path, use_ema) model.load_state_dict(state_dict, strict=strict) def resume_checkpoint(model, checkpoint_path, optimizer=None, loss_scaler=None, log_info=True): resume_epoch = None if os.path.isfile(checkpoint_path): checkpoint = torch.load(checkpoint_path, map_location='cpu') if isinstance(checkpoint, dict) and 'state_dict' in checkpoint: if log_info: _logger.info('Restoring model state from checkpoint...') new_state_dict = OrderedDict() for k, v in checkpoint['state_dict'].items(): name = k[7:] if k.startswith('module') else k new_state_dict[name] = v model.load_state_dict(new_state_dict) if optimizer is not None and 'optimizer' in checkpoint: if log_info: _logger.info('Restoring optimizer state from checkpoint...') optimizer.load_state_dict(checkpoint['optimizer']) if loss_scaler is not None and loss_scaler.state_dict_key in checkpoint: if log_info: _logger.info('Restoring AMP loss scaler state from checkpoint...') loss_scaler.load_state_dict(checkpoint[loss_scaler.state_dict_key]) if 'epoch' in checkpoint: resume_epoch = checkpoint['epoch'] if 'version' in checkpoint and checkpoint['version'] > 1: resume_epoch += 1 # start at the next epoch, old checkpoints incremented before save if log_info: _logger.info("Loaded checkpoint '{}' (epoch {})".format(checkpoint_path, checkpoint['epoch'])) else: model.load_state_dict(checkpoint) if log_info: _logger.info("Loaded checkpoint '{}'".format(checkpoint_path)) return resume_epoch else: _logger.error("No checkpoint found at '{}'".format(checkpoint_path)) raise FileNotFoundError() def load_custom_pretrained(model, default_cfg=None, load_fn=None, progress=False, check_hash=False): r"""Loads a custom (read non .pth) weight file Downloads checkpoint file into cache-dir like torch.hub based loaders, but calls a passed in custom load fun, or the `load_pretrained` model member fn. If the object is already present in `model_dir`, it's deserialized and returned. The default value of `model_dir` is ``/checkpoints`` where `hub_dir` is the directory returned by :func:`~torch.hub.get_dir`. Args: model: The instantiated model to load weights into default_cfg (dict): Default pretrained model cfg load_fn: An external stand alone fn that loads weights into provided model, otherwise a fn named 'laod_pretrained' on the model will be called if it exists progress (bool, optional): whether or not to display a progress bar to stderr. Default: False check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention ``filename-.ext`` where ```` is the first eight or more digits of the SHA256 hash of the contents of the file. The hash is used to ensure unique names and to verify the contents of the file. Default: False """ default_cfg = default_cfg or getattr(model, 'default_cfg', None) or {} pretrained_url = default_cfg.get('url', None) if not pretrained_url: _logger.warning("No pretrained weights exist for this model. Using random initialization.") return cached_file = download_cached_file(default_cfg['url'], check_hash=check_hash, progress=progress) if load_fn is not None: load_fn(model, cached_file) elif hasattr(model, 'load_pretrained'): model.load_pretrained(cached_file) else: _logger.warning("Valid function to load pretrained weights is not available, using random initialization.") def adapt_input_conv(in_chans, conv_weight): conv_type = conv_weight.dtype conv_weight = conv_weight.float() # Some weights are in torch.half, ensure it's float for sum on CPU O, I, J, K = conv_weight.shape if in_chans == 1: if I > 3: assert conv_weight.shape[1] % 3 == 0 # For models with space2depth stems conv_weight = conv_weight.reshape(O, I // 3, 3, J, K) conv_weight = conv_weight.sum(dim=2, keepdim=False) else: conv_weight = conv_weight.sum(dim=1, keepdim=True) elif in_chans != 3: if I != 3: raise NotImplementedError('Weight format not supported by conversion.') else: # NOTE this strategy should be better than random init, but there could be other combinations of # the original RGB input layer weights that'd work better for specific cases. repeat = int(math.ceil(in_chans / 3)) conv_weight = conv_weight.repeat(1, repeat, 1, 1)[:, :in_chans, :, :] conv_weight *= (3 / float(in_chans)) conv_weight = conv_weight.to(conv_type) return conv_weight def load_pretrained(model, default_cfg=None, num_classes=1000, in_chans=3, filter_fn=None, strict=True, progress=False): """ Load pretrained checkpoint Args: model (nn.Module) : PyTorch model module default_cfg (Optional[Dict]): default configuration for pretrained weights / target dataset num_classes (int): num_classes for model in_chans (int): in_chans for model filter_fn (Optional[Callable]): state_dict filter fn for load (takes state_dict, model as args) strict (bool): strict load of checkpoint progress (bool): enable progress bar for weight download """ default_cfg = default_cfg or getattr(model, 'default_cfg', None) or {} pretrained_url = default_cfg.get('url', None) hf_hub_id = default_cfg.get('hf_hub', None) if not pretrained_url and not hf_hub_id: _logger.warning("No pretrained weights exist for this model. Using random initialization.") return if pretrained_url: _logger.info(f'Loading pretrained weights from url ({pretrained_url})') state_dict = load_state_dict_from_url(pretrained_url, progress=progress, map_location='cpu') elif hf_hub_id and has_hf_hub(necessary=True): _logger.info(f'Loading pretrained weights from Hugging Face hub ({hf_hub_id})') state_dict = load_state_dict_from_hf(hf_hub_id) if filter_fn is not None: # for backwards compat with filter fn that take one arg, try one first, the two try: state_dict = filter_fn(state_dict) except TypeError: state_dict = filter_fn(state_dict, model) input_convs = default_cfg.get('first_conv', None) if input_convs is not None and in_chans != 3: if isinstance(input_convs, str): input_convs = (input_convs,) for input_conv_name in input_convs: weight_name = input_conv_name + '.weight' try: state_dict[weight_name] = adapt_input_conv(in_chans, state_dict[weight_name]) _logger.info( f'Converted input conv {input_conv_name} pretrained weights from 3 to {in_chans} channel(s)') except NotImplementedError as e: del state_dict[weight_name] strict = False _logger.warning( f'Unable to convert pretrained {input_conv_name} weights, using random init for this layer.') classifiers = default_cfg.get('classifier', None) label_offset = default_cfg.get('label_offset', 0) if classifiers is not None: if isinstance(classifiers, str): classifiers = (classifiers,) if num_classes != default_cfg['num_classes']: for classifier_name in classifiers: # completely discard fully connected if model num_classes doesn't match pretrained weights del state_dict[classifier_name + '.weight'] del state_dict[classifier_name + '.bias'] strict = False elif label_offset > 0: for classifier_name in classifiers: # special case for pretrained weights with an extra background class in pretrained weights classifier_weight = state_dict[classifier_name + '.weight'] state_dict[classifier_name + '.weight'] = classifier_weight[label_offset:] classifier_bias = state_dict[classifier_name + '.bias'] state_dict[classifier_name + '.bias'] = classifier_bias[label_offset:] model.load_state_dict(state_dict, strict=strict) def extract_layer(model, layer): layer = layer.split('.') module = model if hasattr(model, 'module') and layer[0] != 'module': module = model.module if not hasattr(model, 'module') and layer[0] == 'module': layer = layer[1:] for l in layer: if hasattr(module, l): if not l.isdigit(): module = getattr(module, l) else: module = module[int(l)] else: return module return module def set_layer(model, layer, val): layer = layer.split('.') module = model if hasattr(model, 'module') and layer[0] != 'module': module = model.module lst_index = 0 module2 = module for l in layer: if hasattr(module2, l): if not l.isdigit(): module2 = getattr(module2, l) else: module2 = module2[int(l)] lst_index += 1 lst_index -= 1 for l in layer[:lst_index]: if not l.isdigit(): module = getattr(module, l) else: module = module[int(l)] l = layer[lst_index] setattr(module, l, val) def adapt_model_from_string(parent_module, model_string): separator = '***' state_dict = {} lst_shape = model_string.split(separator) for k in lst_shape: k = k.split(':') key = k[0] shape = k[1][1:-1].split(',') if shape[0] != '': state_dict[key] = [int(i) for i in shape] new_module = deepcopy(parent_module) for n, m in parent_module.named_modules(): old_module = extract_layer(parent_module, n) if isinstance(old_module, nn.Conv2d) or isinstance(old_module, Conv2dSame): if isinstance(old_module, Conv2dSame): conv = Conv2dSame else: conv = nn.Conv2d s = state_dict[n + '.weight'] in_channels = s[1] out_channels = s[0] g = 1 if old_module.groups > 1: in_channels = out_channels g = in_channels new_conv = conv( in_channels=in_channels, out_channels=out_channels, kernel_size=old_module.kernel_size, bias=old_module.bias is not None, padding=old_module.padding, dilation=old_module.dilation, groups=g, stride=old_module.stride) set_layer(new_module, n, new_conv) if isinstance(old_module, nn.BatchNorm2d): new_bn = nn.BatchNorm2d( num_features=state_dict[n + '.weight'][0], eps=old_module.eps, momentum=old_module.momentum, affine=old_module.affine, track_running_stats=True) set_layer(new_module, n, new_bn) if isinstance(old_module, nn.Linear): # FIXME extra checks to ensure this is actually the FC classifier layer and not a diff Linear layer? num_features = state_dict[n + '.weight'][1] new_fc = Linear( in_features=num_features, out_features=old_module.out_features, bias=old_module.bias is not None) set_layer(new_module, n, new_fc) if hasattr(new_module, 'num_features'): new_module.num_features = num_features new_module.eval() parent_module.eval() return new_module def adapt_model_from_file(parent_module, model_variant): adapt_file = os.path.join(os.path.dirname(__file__), 'pruned', model_variant + '.txt') with open(adapt_file, 'r') as f: return adapt_model_from_string(parent_module, f.read().strip()) def default_cfg_for_features(default_cfg): default_cfg = deepcopy(default_cfg) # remove default pretrained cfg fields that don't have much relevance for feature backbone to_remove = ('num_classes', 'crop_pct', 'classifier', 'global_pool') # add default final pool size? for tr in to_remove: default_cfg.pop(tr, None) return default_cfg def overlay_external_default_cfg(default_cfg, kwargs): """ Overlay 'external_default_cfg' in kwargs on top of default_cfg arg. """ external_default_cfg = kwargs.pop('external_default_cfg', None) if external_default_cfg: default_cfg.pop('url', None) # url should come from external cfg default_cfg.pop('hf_hub', None) # hf hub id should come from external cfg default_cfg.update(external_default_cfg) def set_default_kwargs(kwargs, names, default_cfg): for n in names: # for legacy reasons, model __init__args uses img_size + in_chans as separate args while # default_cfg has one input_size=(C, H ,W) entry if n == 'img_size': input_size = default_cfg.get('input_size', None) if input_size is not None: assert len(input_size) == 3 kwargs.setdefault(n, input_size[-2:]) elif n == 'in_chans': input_size = default_cfg.get('input_size', None) if input_size is not None: assert len(input_size) == 3 kwargs.setdefault(n, input_size[0]) else: default_val = default_cfg.get(n, None) if default_val is not None: kwargs.setdefault(n, default_cfg[n]) def filter_kwargs(kwargs, names): if not kwargs or not names: return for n in names: kwargs.pop(n, None) def update_default_cfg_and_kwargs(default_cfg, kwargs, kwargs_filter): """ Update the default_cfg and kwargs before passing to model FIXME this sequence of overlay default_cfg, set default kwargs, filter kwargs could/should be replaced by an improved configuration mechanism Args: default_cfg: input default_cfg (updated in-place) kwargs: keyword args passed to model build fn (updated in-place) kwargs_filter: keyword arg keys that must be removed before model __init__ """ # Overlay default cfg values from `external_default_cfg` if it exists in kwargs overlay_external_default_cfg(default_cfg, kwargs) # Set model __init__ args that can be determined by default_cfg (if not already passed as kwargs) default_kwarg_names = ('num_classes', 'global_pool', 'in_chans') if default_cfg.get('fixed_input_size', False): # if fixed_input_size exists and is True, model takes an img_size arg that fixes its input size default_kwarg_names += ('img_size',) set_default_kwargs(kwargs, names=default_kwarg_names, default_cfg=default_cfg) # Filter keyword args for task specific model variants (some 'features only' models, etc.) filter_kwargs(kwargs, names=kwargs_filter) def build_model_with_cfg( model_cls: Callable, variant: str, pretrained: bool, default_cfg: dict, model_cfg: Optional[Any] = None, feature_cfg: Optional[dict] = None, pretrained_strict: bool = True, pretrained_filter_fn: Optional[Callable] = None, pretrained_custom_load: bool = False, kwargs_filter: Optional[Tuple[str]] = None, **kwargs): """ Build model with specified default_cfg and optional model_cfg This helper fn aids in the construction of a model including: * handling default_cfg and associated pretained weight loading * passing through optional model_cfg for models with config based arch spec * features_only model adaptation * pruning config / model adaptation Args: model_cls (nn.Module): model class variant (str): model variant name pretrained (bool): load pretrained weights default_cfg (dict): model's default pretrained/task config model_cfg (Optional[Dict]): model's architecture config feature_cfg (Optional[Dict]: feature extraction adapter config pretrained_strict (bool): load pretrained weights strictly pretrained_filter_fn (Optional[Callable]): filter callable for pretrained weights pretrained_custom_load (bool): use custom load fn, to load numpy or other non PyTorch weights kwargs_filter (Optional[Tuple]): kwargs to filter before passing to model **kwargs: model args passed through to model __init__ """ pruned = kwargs.pop('pruned', False) features = False feature_cfg = feature_cfg or {} default_cfg = deepcopy(default_cfg) if default_cfg else {} update_default_cfg_and_kwargs(default_cfg, kwargs, kwargs_filter) default_cfg.setdefault('architecture', variant) # Setup for feature extraction wrapper done at end of this fn if kwargs.pop('features_only', False): features = True feature_cfg.setdefault('out_indices', (0, 1, 2, 3, 4)) if 'out_indices' in kwargs: feature_cfg['out_indices'] = kwargs.pop('out_indices') # Build the model model = model_cls(**kwargs) if model_cfg is None else model_cls(cfg=model_cfg, **kwargs) model.default_cfg = default_cfg if pruned: model = adapt_model_from_file(model, variant) # For classification models, check class attr, then kwargs, then default to 1k, otherwise 0 for feats num_classes_pretrained = 0 if features else getattr(model, 'num_classes', kwargs.get('num_classes', 1000)) if pretrained: if pretrained_custom_load: load_custom_pretrained(model) else: load_pretrained( model, num_classes=num_classes_pretrained, in_chans=kwargs.get('in_chans', 3), filter_fn=pretrained_filter_fn, strict=pretrained_strict) # Wrap the model in a feature extraction module if enabled if features: feature_cls = FeatureListNet if 'feature_cls' in feature_cfg: feature_cls = feature_cfg.pop('feature_cls') if isinstance(feature_cls, str): feature_cls = feature_cls.lower() if 'hook' in feature_cls: feature_cls = FeatureHookNet elif feature_cls == 'fx': feature_cls = FeatureGraphNet else: assert False, f'Unknown feature class {feature_cls}' model = feature_cls(model, **feature_cfg) model.default_cfg = default_cfg_for_features(default_cfg) # add back default_cfg return model def model_parameters(model, exclude_head=False): if exclude_head: # FIXME this a bit of a quick and dirty hack to skip classifier head params based on ordering return [p for p in model.parameters()][:-2] else: return model.parameters() def named_apply(fn: Callable, module: nn.Module, name='', depth_first=True, include_root=False) -> nn.Module: if not depth_first and include_root: fn(module=module, name=name) for child_name, child_module in module.named_children(): child_name = '.'.join((name, child_name)) if name else child_name named_apply(fn=fn, module=child_module, name=child_name, depth_first=depth_first, include_root=True) if depth_first and include_root: fn(module=module, name=name) return module def named_modules(module: nn.Module, name='', depth_first=True, include_root=False): if not depth_first and include_root: yield name, module for child_name, child_module in module.named_children(): child_name = '.'.join((name, child_name)) if name else child_name yield from named_modules( module=child_module, name=child_name, depth_first=depth_first, include_root=True) if depth_first and include_root: yield name, module